Search

Facial expression recognition (FER) is a challenging task due to the large appearance variations and the lack of sufficient training data. Conventional deep approaches either learn a good representation through deep models or synthesize images automatically to enlarge the training set. In this paper, we perform both tasks jointly and propose an end-to-end deep model for simultaneous facial expression recognition and facial image synthesis. The proposed model is based on Generative Adversarial Network (GAN) and enjoys several merits. First, the facial image synthesis and facial expression recognition tasks can boost their performance for each other via the unified model. Second, paired images are not required in our facial image synthesis network, which makes the proposed model much more general and flexible. Meanwhile, the generated facial images largely expand the training set and ease the overfitting problem in our FER task. Third, different expressions are encoded in a disentangled manner in a latent space, which enables us to synthesize facial images with arbitrary expressions by exchanging certain parts of their latent identity features. Quantitative and qualitative evaluations on both controlled and in-the-wild FER benchmarks (Multi-PIE, MMI, and RAF-DB) demonstrate the effectiveness of our proposed method on both facial image synthesis and facial expression recognition task.

Video moment retrieval has progressed significantly over the past few years, aiming to search the moment that is most relevant to a given natural language query. Most existing methods are trained in a fully-supervised or a weakly-supervised manner, which requires a time-consuming and expensive manually labeling process. In this work, we propose an alternative approach to achieving video moment retrieval that requires no textual annotations of videos and instead leverages the existing visual concept detectors and a pre-trained image-sentence embedding space. Specifically, we design a video-conditioned sentence generator to produce a suitable sentence representation by utilizing the mined visual concepts in videos. We then design a GNN-based relation-aware moment localizer to reasonably select a portion of video clips under the guidance of the generated sentence. Finally, the pre-trained image-sentence embedding space is adopted to evaluate the matching scores between the generated sentence and moment representations with the knowledge transferred from the image domain. By maximizing these scores, the sentence generator and moment localizer can enhance and complement each other to achieve the moment retrieval task. Experimental results on the Charades-STA and ActivityNet Captions datasets demonstrate the effectiveness of our proposed method.

With the rapid development of social media platforms and the increasing scale of the social media data, the rumor detection task has become vitally important since the authenticity of posts cannot be guaranteed. To date, many approaches have been proposed to facilitate the rumor detection process by utilizing the multi-task learning mechanism, which aims to improve the performance of rumor detection task by leveraging the useful information contained in stance detection task. However, most of the existing approaches suffer from three limitations: (1) only focus on the textual content and ignore the multi-modal information which is key component contained in social media data; (2) ignore the difference of feature space between the stance detection task and rumor detection task, resulting in the unsatisfactory usage of stance information; (3) largely neglect the semantic information hidden in the finegrained stance labels. Therefore, in this paper, we design a Multi-modal Meta Multi-Task Learning (MM-MTL) framework for social media rumor detection task. To make use of multiple modalities, we design a multi-modal post embedding layer which considers both textual and visual content. To overcome the feature sharing problem of the stance detection task and rumor detection task, we propose a meta knowledge-sharing scheme to share some higher meta network layers and capture the metaknowledge behind the multi-modal post. To better utilize the semantic information hidden in the fine-grained stance labels, we employ the attention mechanism to estimate the weight of each reply. Extensive experiments on two Twitter benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance.

Noisy domain adaptation aims to solve the problem that the source dataset contains noisy labels in domain adaptation. Previous methods handle noisy labels by selecting the small-loss samples with inconsistent predictions between two models and discarding the consistent samples, resulting in many noises contained in the selected samples. By jointly considering the consistent and inconsistent samples, we propose a model-agnostic module, named Seek Common Ground While Reserving Differences (SCGWRD), to reduce the impact of noisy samples. The proposed SCGWRD module consists of Seek Common Ground (SCG) component and Reserve Differences (RD) component by utilizing the outputs of two symmetrical domain adaptation models. As the common samples with consistent predictions between two models are more likely to be clean samples, the SCG component applies the small-loss strategy to select the reliable samples with consistent predictions. Unlike SCG, the RD component maintains the divergences between two models with mutual learning and reduces the effect of noisy data using the samples with different predictions and small losses. Evaluations on three benchmarks demonstrate the effectiveness and robustness of the proposed SCGWRD module for noisy domain adaptation.

Recently, unsupervised domain adaptation in person re-identification (ReID) has been widely studied to improve the generalization ability of the ReID model. Some existing methods focus on handling the intra-domain image variations caused by different camera configurations, pose, illumination, and background in target domain. However, they fail to fully mine the underlying consistency constraints contained in unlabeled target dataset. To comprehensively investigate the underlying constraints for unsupervised representation learning, we introduce two consistency constraints to deal with the intra-domain variations, namely instance-ensembling consistency and crossgranularity consistency. Specifically, the instance-ensembling consistency constraint aims to encourage similar features for a given instance and its positive samples. The cross-granularity consistency constraint is designed to share the global and local pseudo labels in multi-granularity feature learning, which can overcome the negative effects caused by the noisy pseudo labels. By combining the advantages of the two constraints, we propose an iterative Intra-domain Consistency Enhancement (ICE) approach based on the Mean Teacher framework to fully mine the two underlying consistency constraints on multi-granularity features. The proposed ICE approach achieves significant improvement compared with the state-of-the-art, which demonstrates the superiority of the two consistency constraints.

Facial expression recognition (FER) has attracted much attention in recent years due to its wide applications. While some progress has been achieved thanks to the emergence of deep learning, the challenge occasioned by pose variations remains. Therefore, most conventional approaches mainly perform FER under laboratory-controlled environment, and the FER in-thewild has received relatively less attention. Pose-invariant FER model would be a possible solution but for a paucity of training data. Sufficient training data with reliable expression labels on FER tasks typically are unavailable. This paper devotes to addressing the problem of how to model pose variations in facial images, and how to leverage noisy data in the web to boost the FER performance. The proposed model is implemented in an end-to-end weakly supervised manner and enjoys several merits. First, we offer a novel pose modeling network to adaptively capture the discrepancy in the deep representation space of facial images under different head poses, and consequently, the poseinvariant expression representations can be learned in our model. Second, the proposed model utilizes massive noisy labeled data to boost the performance of the FER classifier trained on a small set of clean labels. Last, to exploit the reliable information in the noisy data, we formulate a noise modeling network, which is capable of learning the mapping from feature space to the residuals between clean labels and noisy labels. We validate the proposed approach on four public FER benchmarks: AffectNet, RAF-DB, SFEW, and BU-3DFE. Extensive experiments show that the proposed method performs favorably against state-of-the-art methods.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of microvideo sharing platforms. Traditional approaches consider microvideo recommendation as a matching task and ignore the rich relationships among users and micro-videos from various modalities (e.g., visual, acoustic, and textual). Recently, GNNbased approaches show promising performance for the microvideo recommendation task. However, they mainly focus on the homogeneous graph which includes only one type of nodes or relations, and cannot be applied to the heterogeneous graph which consists of users, micro-videos, and related multi-modal information. In this paper, we propose a novel Heterogeneous Hierarchical Feature Aggregation Network (HHFAN) for personalized micro-video recommendation. Our goal is to explore the highly complicated relationship information among users, micro-videos and related multi-modal information from a modality-aware Heterogeneous Information Graph (M-HIG), and thus generate highquality user and micro-video embeddings for recommendation. The proposed model consists of two key components: (1) In data structure level, we build a heterogeneous graph and utilize a random walk based sampling strategy to sample neighbors for users and micro-videos. (2) In representation learning level, we design a hierarchical feature aggregation network including the intra- and inter-type feature aggregation networks to better capture the complex structure and rich semantic information in the heterogeneous graph. We evaluate our method on two realworld datasets and the results demonstrate that the proposed model outperforms the baseline methods.

Though existing cross-domain action recognition methods successfully improve the performance on videos of one view (e.g., egocentric videos) by transferring the knowledge from videos of another view (e.g., exocentric videos), they have limitations in generality because the source and target domains need to be fixed aforehand. In this paper, we propose to solve a more practical task of multi-domain action recognition on egocentricexocentric videos. It aims to transfer knowledge between two domains by a single action recognition model, which can be directly applied on a test video from either of the two domains. To solve this task, we propose memory-based holographic feature learning framework to predict the action classes based on the holographic feature which contains the visual feature extracted from the input video and the complementary feature predicted for the opposite view. To compute the holographic feature, we design a dynamic meta-hallucination module to retrieve the complementary feature from a learnable dual-memory structure, which is built on the view-specific features of egocentric and exocentric videos by writing discriminative centroids of different action classes into the memory. We demonstrate the effectiveness of the proposed method with extensive experimental results on two public datasets. Moreover, the good performances under the semi-supervised setting show the generality of our model.

Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emergence of mobile devices provides the possibility to manage people’s health status in a convenient and instant way. Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging. We propose to model the behavior-related multi-source data streams with a local-global graph, which contains multiple local context sub-graphs to learn short-term local context information with heterogeneous graph neural networks and a global temporal sub-graph to learn long-term dependency with self-attention networks. Then health status is predicted based on the structure-aware representation learned from the local-global behavior graph. We take experiments on the StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.

Transformers, the dominant architecture for natural language processing, have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance. Transformers are sequence-to-sequence models, which use a self-attention mechanism rather than the RNN sequential structure. Thus, such models can be trained in parallel and can represent global information. This study comprehensively surveys recent visual transformer works. We categorize them according to task scenario: backbone design, high-level vision, low-level vision and generation, and multimodal learning. Their key ideas are also analyzed. Differing from previous surveys, we mainly focus on visual transformer methods in low-level vision and generation. The latest works on backbone design are also reviewed in detail. For ease of understanding, we precisely describe the main contributions of the latest works in the form of tables. As well as giving quantitative comparisons, we also present image results for low-level vision and generation tasks. Computational costs and source code links for various important works are also given in this survey to assist further development.

Multi-target multi-camera tracking (MTMCT) targets to generate trajectories of the object that appeared under multiple cameras automatically. MTMCT can be treated as a combination of intra-camera tracking and cross-camera tracking. The existing work only employs the global description to perform the tracklet generating. However, the global description cannot model the local similarity between targets, leading to existing methods not to be robust to occlusion and fast motion. To handle the mentioned problem, we propose an online Optical-based Pose Association (OPA) for multi-target multi-camera tracking. The proposed method utilizes local pose matching to solve the occlusion problem, and applies optical flow to reduce the distance caused by fast motion. For optical-based pose association, we firstly employ OpenPose to generate human pose for each proposal. Then, we utilize the optical flow generated by PWC-Net to adjust the estimated pose for the previous frame. Finally, the modified Object Keypoint Similarity is used to compute the similarity between the pose of the current frame and adjusted pose in the prior frame. Once obtaining the optical-based pose similarity, we combine it with the visual and bounding box spatial similarities to generate the final similarity matrix, and apply the Kuhn-Munkras algorithm for data association. The experiments on the MTMCT and MOT datasets verify the rationality of using human pose information and prove the superiority of the proposed method.

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

Object detection is widely used in object tracking; anchor-free object tracking provides an end-to-end single-object-tracking approach. In this study, we propose a new anchor-free network, the Siamese center-prediction network (SiamCPN). Given the presence of referenced object features in the initial frame, we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations. Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction, SiamCPN directly obtains all information required for tracking, greatly simplifying the model. A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net. The model can accurately predict object location, implement appropriate corrections, and regress the size of the target bounding box. Compared to other leading Siamese networks, SiamCPN is simpler, faster, and more efficient as it uses fewer hyperparameters. Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks, and is comparable to other excellent trackers on LaSOT, VOT2016, and OTB-100 while improving inference speed 1.5 to 2 times.

With the surge of images in the information era, people demand an effective and accurate way to access meaningful visual information. Accordingly, effective and accurate communication of information has become indispensable. In this article, we propose a content-based approach that automatically generates a clear and informative visual summarization based on design principles and cognitive psychology to represent image collections. We first introduce a novel method to make representative and nonredundant summarizations of image collections, thereby ensuring data cleanliness and emphasizing important information. Then, we propose a tree-based algorithm with a two-step optimization strategy to generate the final layout that operates as follows: (1) an initial layout is created by constructing a tree randomly based on the grouping results of the input image set; (2) the layout is refined through a coarse adjustment in a greedy manner, followed by gradient back propagation drawing on the training procedure of neural networks. We demonstrate the usefulness and effectiveness of our method via extensive experimental results and user studies. Our visual summarization algorithm can precisely and efficiently capture the main content of image collections better than alternative methods or commercial tools.

In the past years, pedestrian detection has achieved significant progress via improving the visual description. However, the visual description is not robust to discover the occluded pedestrian, which is the bottleneck of the existing pedestrian methods. Targeting to overcome the shortcoming of visual description, we employ the human pose information, which is complementary to the visual description, to address the occlusion and false positive failure problems in pedestrian detection. The advantage of using human pose information is that the pose estimation model can localize the local part of the pedestrian once the pedestrian is occluded. By embedding the human pose information with the visual description, we propose a novel Pose-Embedding Network for pedestrian detection, which consists of two components: a Region Proposal Network, and a Pedestrian Recognization Network. The Region Proposal Network targets to generate lots of candidate proposals and corresponding confidence scores. Once obtaining the candidate proposals, the Pedestrian Recognization Network is proposed to distinguish pedestrian proposals by taking the visual information and pose information into consideration to refine the confidence scores and eliminate the false positives. Given the proposal image, the visual information is extracted with the Visual Feature Module. The Human Pose Module, which is proposed based on the pose estimation model, is used to predict the pose information. Further, the Classification Module is employed to fuse the visual and pose information and generates a pose-embedding pedestrian description. Extensive experiments on three challenging datasets, i.e., Caltech, CityPersons, and COCOPersons, show that the proposed approach achieves a significant improvement upon the state-of-the-art methods.

Over the past few years, graph representation learning (GRL) has received widespread attention on the feature representations of the non-Euclidean data. As a typical model of GRL, graph convolutional networks (GCN) fuse the graph Laplacian-based static sample structural information. GCN thus generalizes convolutional neural networks to acquire the sample representations with the variously high-order structures. However, most of existing GCN-based variants depend on the static data structural relationships. It will result in the extracted data features lacking of representativeness during the convolution process. To solve this problem, dynamic graph learning convolutional networks (DGLCN) on the application of semi-supervised classification are proposed. First, we introduce a definition of dynamic spectral graph convolution operation. It constantly optimizes the high-order structural relationships between data points according to the loss values of the loss function, and then fits the local geometry information of data exactly. After optimizing our proposed definition with the one-order Chebyshev polynomial, we can obtain a single-layer convolution rule of DGLCN. Due to the fusion of the optimized structural information in the learning process, multi-layer DGLCN can extract richer sample features to improve classification performance. Substantial experiments are conducted on citation network datasets to prove the effectiveness of DGLCN. Experiment results demonstrate that the proposed DGLCN obtains a superior classification performance compared to several existing semi-supervised classification models.

In this paper, we present a method called density-aware convolutional neural network (DensityCNN) to perform the crowd counting task in various crowded scenes. The key idea of the DensityCNN is to utilize high-level semantic information to provide guidance and constraint when generating density maps. To this end, we implement the DensityCNN by adopting a multi-task CNN structure to jointly learn density-level classification and density map estimation. The density-level classification task learns multi-channel semantic features that are aware of the density distributions of the input image. This task is accomplished via our specially designed group-based convolutional structure in a supervised learning manner. In the density map estimation task, these semantic features are deployed together with high-dimension convolutional features to generate density maps with lower count errors. Extensive experiments on four challenging crowd datasets (ShanghaiTech, UCF_CC_50, UCF-QNCF, and WorldExpo’10) and one vehicle dataset TRANCOS demonstrate the effectiveness of the proposed method.

Nowadays, community-based question answering (CQA) systems are popular and have accumulated a large number of questions and answers provided by users. How to accurately match relevant answers for a given question is an essential function in CQA tasks. Recent effective methods utilize word-pair interactions between questions and answers for CQA matching. However, these approaches usually encode questions and answers independently and ignore the fact that they can complement and enhance each other to provide better representations and thus more implicit interactions can be captured. In addition, the visual information, social information and the variable-length problem are usually ignored by most existing approaches. In this paper, a Social-aware Multi-modal Co-attention Convolutional Matching method (SMCACM) is proposed, which models the multi-modal content and social context of questions and answers in a unified framework for CQA matching. A novel co-attention network is proposed to extract complementary information from questions and answers to enhance each other for obtaining better representations, through which our model can capture more implicit interactions between questions and answers. In addition to textual content, our model uses object detection techniques and a meta-path based heterogeneous social representation learning approach to take advantage of the visual content and social context in CQA systems, respectively. Finally, a pooling-based convolutional matching network is designed to infer the matching score based on the complemented questions and answers, which can accept variable-length answers as inputs without padding or cutting. Experimental results on two real-world datasets demonstrate the superior performance of SMCACM compared with other state-of-the-art algorithms.

Among different 3D data representations, point cloud stands out for its efficiency and flexibility. Hence, many researchers have been involved in the point cloud analysis recently. Existing approaches for point cloud segmentation task typically suffer from two limitations: 1) They usually treat different neighbor points as equals which cannot characterize the correlation between the center point and its neighborhoods well. Moreover, different parts may have different local structures for a point cloud, but they just learn a single representation space which is not sufficient and stable. 2) They often capture hierarchical information by heuristic sampling approaches which cannot reveal the spatial relationships of points well to learn global features. To overcome these limitations, we propose a novel hierarchical attentive pooling graph network (HAPGN) which utilizes the gated graph attention network (GGAN) and hierarchical graph pooling module (HiGPool) as building blocks for point cloud segmentation. Specifically, GGAN can highlight not only the importance of different neighbor points but also the importance of different representation spaces to enhance the local feature extraction. HiGPool is a novel pooling module that can capture the spatial layouts of points to learn the hierarchical features adequately. Experimental results on the ShapeNet part dataset and S3DIS dataset show that HAPGN can achieve superior performance over the state-of-the-art segmentation approaches. Furthermore, we also combine our proposed HiGPool with some recent approaches for point cloud classification and achieve better results on the ModelNet40 dataset.

This paper addresses video highlight detection which aims to select a small subset of frames according to user's major or special interest. The performances of conventional methods highly depend on large-scale manually labeled training data which are time-consuming and labor-intensive to collect. To deal with this problem, we trace back to the original problem definition and find that whether a user is interested in a specific video segment heavily depends on human's subjective emotions. Leveraging this insight, we introduce an emotion knowledge driven video detection framework for modeling human's general emotion and inferencing highlight strength. Firstly, we obtain the concept-level representation of the video clip with a front-end network. The concepts are used as nodes to build an emotion-related knowledge graph, and their relationships in the graph are modeled via external public knowledge graphs. Then we adopt Siamese GCNs to model the dependencies between nodes in the graph and propagate messages along the edges. Finally, we compute the emotion-aware representation of the video clip based on the GCN layers and further use it to predict the highlight score. Our framework, including the front-end network, graph convolution layers and the highlight mapping network, can be trained in an end-to-end manner with the constraint of a ranking loss. Experiments on two benchmark datasets show that our proposed method performs favorably against the state-of-the-art methods.

Visual question answering by using information from multiple modalities has attracted more and more attention in recent years. However, it is a very challenging task, as the visual content and natural language have quite different statistical properties. In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering. In AMN, we propose to learn multimodal feature representations by finding a more coherent subspace for video clips and the corresponding texts (e.g., subtitles and questions) based on generative adversarial networks. Moreover, a self-attention mechanism is developed to enforce our newly introduced consistency constraint in order to preserve the self-correlation between the visual cues of the original video clips in the learned multimodal representations. Extensive experiments on the benchmark MovieQA and TVQA datasets show the effectiveness of our proposed AMN over other published state-of-the-art methods.

The self-ensembling methods have achieved amazing performance for semi-supervised representation learning and domain adaptation. However, the disadvantage of these methods is that the teacher network is tightly coupled with the student network, which limits the descriptive ability of the self-ensembling model. To overcome the coupling effect between the teacher network and the student network, we propose a novel Triplet Ensemble Student-Teacher (TEST) model for unsupervised person re-identification, which consists of one teacher network $T$ and two student networks $S1$ and $S2$ . Similar to the traditional self-ensembling model, the student network $S1$ is applied to update the teacher network $T$ . Furthermore, a closed-loop learning mechanism is built in the TEST model by imposing an ensemble consistent constraint between $T$ and $S2$ , and performing a heterogeneous co-teaching procedure between $S1$ and $S2$ . With the closed-loop learning mechanism, the TEST model can loosen the constraint between the teacher $T$ and the student $S1$ , and enhance the descriptive ability of $S1$ . Besides, the knowledge exchange between $S1$ and $S2$ can ensure that the two student networks can elegantly deal with the noisy labels and avoid coupling. By training the TEST model with the clustering-generated pseudo labels, we can achieve effective and robust representation learning for unsupervised person re-identification. The evaluations on three widely-used benchmarks show that our approach can achieve significant performance compared with state-of-the-art methods.

We address the problem of video-text retrieval that searches videos via natural language description or vice versa. Most state-of-the-art methods only consider cross-modal learning for two or three data points in isolation, ignoring to get benefit from the structural information of other data points from a global view. In this paper, we propose to exploit the comprehensive relationships among cross-modal samples via Graph Neural Networks (GNN). To improve the discriminative ability for accurately finding the positive sample, a Coarse-to-Fine GNN is constructed, which can progressively optimize the retrieval results via multi-step reasoning. Specifically, we first adopt heuristic edge features to represent relationships. Then we design a scoring module in each layer to rank the edges connected to the query node and drop the edges with lower scores. Finally, to alleviate the class imbalance issue, we propose a random-drop focal loss to optimize the whole framework. Extensive experimental results show that our method consistently outperforms the state-of-the-arts on four benchmarks.

Art painting evaluation is sophisticated for a novice with no or limited knowledge on art criticism, and history. In this study, we propose the concept of representativity to evaluate paintings instead of using professional concepts, such as genre, media, and style, which may be confusing to non-professionals. We define the concept of representativity to evaluate quantitatively the extent to which a painting can represent the characteristics of an artists creations. We begin by proposing a novel deep representation of art paintings, which is enhanced by style information through a weighted pooling feature fusion module. In contrast to existing feature extraction approaches, the proposed framework embeds painting styles, and authorship information, and learns specific artwork characteristics in a single framework. Subsequently, we propose a graph-based learning method for representativity learning, which considers intra-category, and extra-category information. In view of the significance of historical factors in the art domain, we introduce the creation time of a painting into the learning process. User studies demonstrate our approach helps the public effectively access the creation characteristics of artists through sorting paintings by representativity from highest to lowest.

Social media has revolutionized the way people understand and keep track of real-world events. Various related multimedia information in different modalities such as texts, images and videos is updated on social media and reflects the events. These quantities of information distributes on different Online Social Networks (OSNs), which provides rich, wide coverage, comprehensive information about the trending events. Faced with such large amounts of data, searching has become a handy tool for event understanding and tracking on social media. However, existing single-OSN search mainly involves with single modality on single platform. Moreover, most OSNs usually focus on biased perspective of events, which significantly limits the coverage and diversity of single-OSN based event search. In this paper, we introduce a novel cross-OSN framework to help integrate these cross-OSN information regarding the same event and provide an immersive experience for information retrieval. Since social media information is widely distributed in different OSNs where semantic gap exists among these heterogeneous spaces, we propose to utilize hashtag and tag, which are user-generated metadata for organizing and labeling in many OSNs, as bridges to connect between different OSNs. In our four-stage solution framework, various methods are adopted for hashtag and tag filtering, search results representation, clustering and demonstration. Given an event query, in the first stage we generate related items with corresponding tags and hashtags from OSNs and filter the hashtags and tags we need. Then, topical representation is generated for hashtag and tag. The third stage leverages the derived representation for cross-OSN hashtag and tag clustering. Finally, demonstration for each query is produced and the results are organized hierarchically. Experiments on a dataset containing hundreds of search queries and related items demonstrate the effectiveness of our cross-OSN event search framework.

With the rapid development of social media and the increasing scale of social media data, the rumor detection on social media platforms has become vitally crucial. The key challenges for rumor detection on social media platforms are how to identify rumors deeply entangled with the specific content and how to detect rumors for the emerging social media events without labeled data. Unfortunately, most of the existing approaches can hardly handle these challenges since they tend to learn event-specific features and cannot transfer the learned features to newly emerged events. To tackle the above challenges, we propose a novel Multimodal Disentangled Domain Adaption (MDDA) method which can derive event-invariant features and thus benefit the detection of rumors on emerging social media events. The model consists of two components: the multimodal disentangled representation learning and the unsupervised domain adaptation. The multimodal disentangled representation learning is responsible for disentangling the multimedia posts into the content features and the rumor style features, and removing the content-specific features from post representation. The unsupervised domain adaptation aims to filter out the event-specific features and keep shared rumor style features among events. Based on the final event-invariant rumor style features, we train a robust social media rumor detector that can transfer knowledge from source events to the target events, which can perform well on the newly emerged events. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection model outperforms state-of-the-art methods.

We address the problem of few-shot video classification that learns classifiers for novel concepts from only a few examples. Most current methods ignore to explicitly consider the relations in both intra-video and inter-video domains, thus cannot take full advantage of the structural information in few-shot learning. In this paper, we propose to exploit the comprehensive intra-video and inter-video relations via Graph Neural Networks (GNNs). To improve the discriminative ability for accurately selecting the representative video content and refining video relations, a Dual-Pooling GNN (DPGNN) is constructed, which stacks customized graph pooling layers in a hierarchical fashion. Specifically, to select the most representative frames in a video, we build intra-video graphs and utilize a node pooling module to extract robust video-level features. We construct an inter-video graph by taking the video-level features as nodes. By designing an edge pooling module, the proposed method can adaptively eliminate the negative relations in the inter-video graph. Extensive experimental results show that our method consistently outperforms the state-of-the-art on two benchmarks.

We address the problem of unsupervised video summarization that automatically selects key video clips. Most state-of-the-art approaches suffer from two issues: (1) they model video clips without explicitly exploiting their relations, and (2) they learn soft importance scores over all the video clips to generate the summary representation. However, a meaningful video summary should be inferred by taking the relation-aware context of the original video into consideration, and directly selecting a subset of clips with a hard assignment. In this paper, we propose to exploit clip-clip relations to learn relation-aware hard assignments for selecting key clips in an unsupervised manner. First, we consider the clips as graph nodes to construct an assignment-learning graph. Then, we utilize the magnitude of the node features to generate hard assignments as the summary selection. Finally, we optimize the whole framework via a proposed multi-task loss including a reconstruction constraint, and a contrastive constraint. Extensive experimental results on three popular benchmarks demonstrate the favourable performance of our approach.

Cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications, thanks to low storage cost and fast query speed. However, preserving the content similarities in finite-length hash codes between different data modalities is still challenging due to the existing heterogeneity gap. To further address the crucial bottleneck, we propose a Multi-Level Correlation Adversarial Hashing (MLCAH) algorithm to integrate the multi-level correlation information into hash codes. The proposed MLCAH model enjoys several merits. First, to the best of our knowledge, it is the early attempt of leveraging the multi-level correlation information for cross-modal hashing retrieval. Second, we propose global and local semantic alignment mechanisms, which can effectively encode multi-level correlation information, including global information, local information, and label information into hash codes. Third, a label-consistency attention mechanism with adversarial training is designed for exploiting the local cross-modality similarity from multi-modality data. Extensive evaluations on four benchmarks demonstrate that the proposed model brings significant improvements over several state-of-the-art cross-modal hashing methods.

With the ever-growing video categories, Zero-Shot Learning (ZSL) in video classification has drawn considerable attention in recent years. To transfer the learned knowledge from seen categories to unseen categories, most existing methods resort to an implicit model that learns a projection between visual features and semantic category-representations. However, such methods ignore the explicit relationships among video instances and categories, which impede the direct information propagation in a Category-Instance graph (CI-graph) consisting of both instances and categories. In fact, exploring the structure of the CI-graph can capture the invariances of the ZSL task with good generality for unseen instances. Inspired by these observations, we propose an end-to-end framework to directly and collectively model the relationships between category-instance, category-category, and instance-instance in the CI-graph. Specifically, to construct node features of this graph, we adopt object semantics as a bridge to generate unified representations for both videos and categories. Motivated by the favorable performance of Graph Neural Networks (GNNs), we design a Category-Instance GNN (CI-GNN) to adaptively model the structure of the CI-graph and propagate information among categories and videos. With the task-driven message passing process, the learned model is able to transfer label information from categories towards unseen videos. Extensive experiments on four video datasets demonstrate the favorable performance of the proposed framework.

Recognizing activities from egocentric multimodal data collected by wearable cameras and sensors, is gaining interest, as multimodal methods always benefit from the complementarity of different modalities. However, since high-dimensional videos contain rich high-level semantic information while low-dimensional sensor signals describe simple motion patterns of the wearer, the large modality gap between the videos and the sensor signals raises a challenge for fusing the raw data. Moreover, the lack of large-scale egocentric multimodal datasets due to the cost of data collection and annotation processes makes another challenge for employing complex deep learning models. To jointly deal with the above two challenges, we propose a knowledge-driven multimodal activity recognition framework that exploits external knowledge to fuse multimodal data and reduce the dependence on large-scale training samples. Specifically, we design a dual-GCLSTM (Graph Convolutional LSTM) and a multi-layer GCN (Graph Convolutional Network) to collectively model the relations among activities and intermediate objects. The dual-GCLSTM is designed to fuse temporal multimodal features with top-down relation-aware guidance. In addition, we apply a co-attention mechanism to adaptively attend to the features of different modalities at different timesteps. The multi-layer GCN aims to learn relation-aware classifiers of activity categories. Experimental results on three publicly available egocentric multimodal datasets show the effectiveness of the proposed model.

Person re-identification aims to match person of interest under non-overlapping camera views. Therefore, how to generate a robust and discriminative representation is crucial for person re-identification. Mining local clues from human body parts to describe pedestrians has been extensively studied in existing methods. However, existing methods locate human body parts coarsely and do not consider the relations among different local parts. To address the above problem, we propose a Part-based Structured Representation Learning (PSRL) for better exploiting local clues to improve the person representation. There are two important modules in our architecture: Local Semantic Feature Extraction and Structured Person Representation Learning. The Local Semantic Feature Extraction module is designed to extract local features from human body semantic regions. After obtaining the local features, the Structured Person Representation Learning is proposed to fuse the local features by considering the person structure. To model the underlying person structure, a graph convolutional network is employed to capture the relations of different semantic regions. The generated structured feature encodes underlying person structure information, and local semantic feature can solve the misalignment problem caused by pose variations in feature matching. By combining them together, we can improve the descriptive ability of the generated representation. Extensive evaluations on four standard benchmarks show that our proposed method achieves competitive performance against state-of-the-art methods.

With the development of the Internet, more and more creators publish articles on social media. How to automatically filter high quality content from a large number of multimedia articles is one of the core functions of information recommendation, search engine, and other systems. However, existing approaches typically suffer from two limitations: (1) They usually model content as word sequences, which ignores the semantics provided by non-consecutive phrases, long-distance word dependency, and visual information. (2) They rely on a large amount of manually annotated data to train a quality assessment model while users may only provide labels of interest in a single class for a small number of samples in reality. To address these limitations, we propose a Multimodal Graph Convolutional Networks (MGCN) to model the semantic representations in a unified framework for High Quality Content Recognition. Instead of viewing text content as word sequences, we convert them into graphs, which can model non-consecutive phrases and long-distance word dependency for better obtaining the composition of semantics. Besides, visual content is also modeled into the graphs to provide complementary semantics. A well-designed graph convolutional network is proposed to capture the semantic representations based on these graphs. Furthermore, we employ a non-negative risk estimator for high quality content recognition and the loss is back-propagated for model learning. Experiments on real datasets validate the effectiveness of our approach.

A critical bottleneck in pedestrian detection is the detection of small-scale pedestrians, which have low contrast and blurry shapes in images and videos. Considered that the body shape of a pedestrian is always rectangular (the height is greater than the width), we propose an asymmetric multi-stage network (AMS-Net) for small-scale pedestrian detection. The proposed method has two main advantages. (1) It considers the asymmetry of a pedestrian’s body shape in pedestrian detection. The rectangular anchors are used to generate various rectangular proposals that have a height greater than the width. In addition, asymmetric rectangular convolution kernels are adopted for capturing the compact features of the pedestrian body. (2) The proposed AMS-Net gradually rejects the non-pedestrian boxes according to coarse-to-fine features in a three-stage framework. The proposed AMS-Net significantly improves the performance of pedestrian detection on the Far subset of the Caltech testing set (the miss rate decreases from 60.79% to 51.36%). It also achieves competitive performance on the INRIA, ETH, KITTI and CityPersons benchmarks.

With the accumulation of data on the Internet and progress in representation learning techniques, knowledge priors learned from a large-scale knowledge base has been increasingly used in probabilistic topic models. However, it is challenging to learn interpretable topics and a discriminative event representation based on multi-modal information. To address these issues, we propose a knowledge priors- and max-margin-based topic model for multi-modal social event analysis, called the KGE-MMSLDA, in which feature representation and knowledge priors are jointly learned. Our model has three main advantages over current methods: (1) It integrates additional knowledge from external knowledge base into a unified topic model in which the max-margin classifier, and multi-modal information are exploited to increase the number of event descriptions obtained. (2) We mined knowledge priors from over 74,000 web documents. Multi-modal data with these knowledge priors are then incorporated into the topic model to increase the number of coherent topics learned. (3) A large-scale multi-modal dataset (containing 10 events, where each event contained approximately 7,000 Flickr pages) was collected and has been released publicly for event topic mining and classification research. In comparative experiments, the proposed method outperformed state-of-the-art models on topic coherence, and obtained a classification accuracy of 85.1%.

We present a practical approach to improve the precision of face alignment for a single image. Recently, face alignment is deemed as a regression problem, and convolutional neural networks (CNNs) or recurrent neural networks (RNNs) are utilized to predict the coordinates of facial landmarks. However, most existing methods only adopt Euclidean loss as the optimization target for each landmark, and neglect the correlations between them, which we think may be inappropriate. To address this issue, in this paper, we introduce a novel Relative Coordinates Constraint (RCC) loss function for face alignment, which considers the relative coordinates between any pairs of landmarks as a new supervision signal. More importantly, we prove that the proposed RCC loss function is trainable and can be easily incorporated in existing CNNs optimization procedure. With the joint supervision of Euclidean loss and RCC loss, we train a robust and light CNNs framework for face alignment. Extensive experimental results on several datasets show that the precision of face alignment improved significantly by the proposed RCC loss and quantitative results are comparable to state-of-the-art methods (mean error 5.39 on 300-W and 6.99 on AFLW). In addition, the proposed framework is also an efficient solution (300 FPS on CPU). We share the implementation code of our proposed methods at https://github.com/nianfudong/RCC-loss .

Most of existing multimodal event classification methods fuse the traditional hand-crafted features with some manually defined weights, which may be not suitable to the event classification task with large amounts of photos. Besides, the feature extraction and event classification model are always performed separately, which cannot capture the most useful features to describe the semantic concepts of complex events. To deal with these issues, we propose a novel discriminative multimodal embedding (DME) model for event classification in user generated photos by jointly learning the representation together with the classifier in a unified framework. In the proposed DME model, we can effectively resolve the multimodal, intra-class variation and inter-class confusion challenges by using the contrastive constraints on the multimodal event data. Extensive experimental results on two collected datasets demonstrate the effectiveness of the proposed DME model for event classification.

It is critical to comprehensively and efficiently learn user preferences for an effective sequential recommender system. Existing sequential recommendation methods mainly focus on modeling local preference from users’ historical behaviors, which largely ignore the global context information from the heterogeneous information network. This prevents a comprehensive user preference representation. To address these issues, we propose a joint learning approach to incorporate global context with local preferences efficiently. The proposed approach introduces meta-paths from a heterogeneous information network to capture the global context information, and the position-based self-attention mechanism is adopted to model the local preference representation efficiently. Compared with the methods that only consider the local preference, our proposed method takes the advantages of incorporating global context information, which extracts structural features that captures relevant semantics to construct users’ global preference representation for the sequential recommendation. We further adopt a co-attention mechanism to model complex interactions between global context and users’ historical behaviors for better user representations. Quantitative and qualitative experimental evaluations are conducted on nine large-scale Amazon datasets and a multi-modal Zhihu dataset. The promising results demonstrate the effectiveness of the proposed model.

With the increasing prevalence of portable computing devices, browsing unedited videos is time-consuming and tedious. Video highlight detection has the potential to significantly ease this situation, which discoveries moments of user's major or special interest in a video. Existing methods suffer from two problems. Firstly, most existing approaches only focus on learning holistic visual representations of videos but ignore object semantics for inferring video highlights. Secondly, current state-of-the-art approaches often adopt the pairwise ranking-based strategy, which cannot enjoy the global information to infer highlights. Therefore, we propose a novel video highlight framework, named VH-GNN, to construct an object-aware graph and model the relationships between objects from a global view. To reduce computational cost, we decompose the whole graph into two types of graphs: a spatial graph to capture the complex interactions of object within each frame, and a temporal graph to obtain object-aware representation of each frame and capture the global information. In addition, we optimize the framework via a proposed multi-stage loss, where the first stage aims to determine the highlight-probability and the second stage leverage the relationships between frames and focus on hard examples from the former stage. Extensive experiments on two standard datasets strongly evidence that VH-GNN obtains significant performance compared with state-of-the-arts.

Facial expression recognition, face synthesis, and face alignment are three coherently related tasks and can be solved in a joint framework. To achieve this goal, in this paper, we propose a novel end-to-end deep learning model by exploiting the expression code, geometry code and generated data jointly for simultaneous pose-invariant facial expression recognition, face image synthesis, and face alignment. The proposed deep model enjoys several merits. First, to the best of our knowledge, this is the first work to address these three tasks jointly in a unified deep model to complement and enhance each other. Second, the proposed model can effectively disentangle the global and local identity representation from different expression and geometry codes. As a result, it can automatically generate facial images with different expressions under arbitrary geometry codes. Third, these three tasks can further boost their performance for each other via our model. Extensive experimental results on three standard benchmarks demonstrate that the proposed deep model performs favorably against state-of-the-art methods on the three tasks.

Input scale plays an important role in modern detection frameworks, and an optimal training scale for images exists empirically. However, the optimal one usually cannot be reached in facing extremely large images under the memory constraint. In this study, we explore the scale effect inside the object detection pipeline and find that feature upsampling with the introduction of high-resolution information benefits the detection. Compared with direct input upscaling, feature upsampling trades a small performance loss for a large amount of memory savings. From these observations, we propose a self-supervised feature augmentation network, which takes downsampled images as inputs and aims to generate comparable features with the ones when feeding upscaled images to networks. We present a guided feature upsampling module, which takes downsampled images as inputs, to learn upscaled feature representations with the supervision of real large features acquired from upscaled images. In a self-supervised learning manner, we can introduce detailed information of images to the network. For an efficient feature upsampling, we design a residualized sub-pixel convolution block based on a sub-pixel convolution layer, which involves considerable information in upsampling process. Experiments on Mapillary Vistas Dataset (MVD), Cityscapes, and COCO are conducted to demonstrate the effectiveness of our method. On the MVD and Cityscapes detection benchmarks, in which the images are extremely large, our method surpasses current approaches. On COCO, the proposed method obtains comparable results to existing methods but with higher efficiency.

With the availability of easy access to the Internet, and the proliferation of various mobile devices, people can get access to mobile multimedia ubiquitously in order to have a desktop experience. Mobile multimedia refers to different types of multimedia content (e.g., text, images, audios, and videos), which are accessed via heterogeneous mobile devices, such as mobile phones, portable devices, and smartphones. Mobile multimedia services and applications have tremendous potential in the healthcare sector. Currently, as the healthcare sector is increasingly dependent on mobile multimedia services and applications, many challenges remain unsolved for the successful deployment of mobile multimedia services in order to have a balance between power-limited mobile devices and resource hungry medical multimedia content. This Special Section aims to theme innovative research achievements in the field of related techniques, applications, services, and systems for mobile multimedia healthcare.

In this paper, we consider the task of action anticipation on egocentric videos. Previous methods ignore explicit modeling of the global context relation among past and future actions, which is not an easy task due to the vacancy of unobserved videos. To solve this problem, we propose a Multimodal Global Relation Knowledge Distillation (MGRKD) framework to distill the knowledge learned from full videos to improve the action anticipation task on partially observed videos. The proposed MGRKD has a teacher-student learning strategy, where either the teacher or student model has three branches of global relation graph networks (GRGN) to explore the pairwise relations between past and future actions based on three kinds of features (i.e., RGB, motion or object). The teacher model has a similar architecture with the student model, except that the teacher model uses true feature of the future video snippet to build the graph in GRGN while the student model uses a progressive GRU to predict an initialized node feature of future snippet in GRGN. Through the teacher-student learning strategy, the discriminative features and relation knowledge of the past and future actions learned in the teacher model can be distilled to the student model. The experiments on two egocentric video datasets EPIC-Kitchens and EGTEA Gaze+ show that the proposed framework achieves state-of-the-art performances.

Diagram question answering (DQA) is an effective way to evaluate the reasoning ability for diagram semantic understanding, which is a very challenging task and largely understudied compared with natural images. Existing separate two-stage methods for DQA are limited in ineffective feedback mechanisms. To address this problem, in this paper, we propose a novel structural parsing-integrated Hierarchical Multi-Task Learning (HMTL) model for diagram question answering based on a multi-modal transformer framework. In the proposed paradigm of multi-task learning, the two tasks of diagram structural parsing and question answering are in the different semantic levels and equipped with different transformer blocks, which constituents a hierarchical architecture. The structural parsing module encodes the information of constituents and their relationships in diagrams, while the diagram question answering module decodes the structural signals and combines question-answers to infer correct answers. Visual diagrams and textual question-answers are interplayed in the multi-modal transformer, which achieves cross-modal semantic comprehension and reasoning. Extensive experiments on the benchmark AI2D and FOODWEBS datasets demonstrate the effectiveness of our proposed HMTL over other state-of-the-art methods.

We investigate the problem of weakly-supervised video object grounding (WSVOG), where only the video-sentence annotations are provided for training. It aims at localizing the queried objects described in the sentence to visual regions in the video. Despite the recent progress, existing approaches have not fully exploited the potential of the description sentences for cross-modal alignment in two aspects: (1) Most of them extract objects from the description sentences and represent them with fixed textual representations. While achieving promising results, they do not make full use of the contextual information in the sentence. (2) A few works have attempted to utilize contextual information to learn object representations, but found a significant decrease in performance due to the unstable training in cross-modal alignment. To address the above issues, in this paper, we propose a Stable Context Learning (SCL) framework for WSVOG which jointly enjoys the merits of stable learning and rich contextual information. Specifically, we design two modules named Context-Aware Object Stabilizer module and Cross-Modal Alignment Knowledge Transfer module, which are cooperated together to inject contextual information to stable object concepts in text modality and transfer contextualized knowledge in cross-modal alignment. Our approach is finally optimized under a frame-level MIL paradigm. Extensive experiments on three popular benchmarks demonstrate its significant effectiveness.

Recognizing human emotions from videos has attracted significant attention in numerous computer vision and multimedia applications, such as human-computer interaction and health care. It aims to understand the emotional response of humans, where candidate emotion categories are generally defined by specific psychological theories. However, with the development of psychological theories, emotion categories become increasingly diverse and fine-grained, samples are also increasingly difficult to collect. In this paper, we investigate a new task of zero-shot video emotion recognition, which aims to recognize rare unseen emotions. Specifically, we propose a novel multimodal protagonist-aware transformer network, which is composed of two branches: one is equipped with a novel dynamic emotional attention mechanism and a visual transformer to learn better visual representations; the other is an acoustic transformer for learning discriminative acoustic representations. We manage to align the visual and acoustic representations with semantic embeddings of fine-grained emotion labels through jointly mapping them into a common space under a noise contrastive estimation objective. Extensive experimental results on three datasets demonstrate the effectiveness of the proposed method.

We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc. These infrastructures enable tf_geometric to support single-graph computation, multi-graph computation, graph mini-batch, distributed training, etc.; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as transductive node classification, inductive node classification, link prediction, and graph classification. Based on the kernel libraries, tf_geometric implements a variety of popular GNN models for different tasks. To facilitate the implementation of GNNs, tf_geometric also provides some other libraries for dataset management, graph sampling, etc. Different from existing popular GNN libraries, tf_geometric provides not only Object-Oriented Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to handle advanced graph deep learning tasks such as graph meta-learning. The APIs of tf_geometric are friendly, and they are suitable for both beginners and experts. In this paper, we first present an overview of tf_geometric's framework. Then, we conduct experiments on some benchmark datasets and report the performance of several popular GNN models implemented by tf_geometric., Comment: 7 pages, 5 figures